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A comprehensive artificial intelligence framework for dental diagnosis and charting.

Tanjida Kabir1, Chun-Teh Lee2, Luyao Chen1

  • 1The University of Texas Health Science Center at Houston, School of Biomedical Informatics, Houston, TX, USA.

BMC Oral Health
|November 10, 2022
PubMed
Summary

This study introduces an AI framework that automatically identifies tooth numbers in dental X-rays and organizes them into a full mouth series. This technology enhances diagnostic accuracy and clinical workflow for dentists.

Keywords:
Computer visionDeep learning/machine learningDental informatics/bioinformaticsImagingRadiography

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Area of Science:

  • Artificial Intelligence in Dentistry
  • Medical Image Analysis
  • Radiographic Interpretation

Background:

  • Developing automated systems for dental radiographic analysis is crucial for improving diagnostic efficiency.
  • Accurate tooth identification and arrangement are fundamental for comprehensive dental assessments.

Purpose of the Study:

  • To create an artificial intelligence (AI) guided framework for recognizing tooth numbers in panoramic and intraoral radiographs.
  • To automatically arrange intraoral radiographs into a full mouth series (FMS) template.
  • To enable integration with disease diagnosis models for enhanced clinical examinations.

Main Methods:

  • Utilized image segmentation to generate masks of bone, teeth, and cementoenamel junction (CEJ) lines.
  • Employed image analysis to detect and extract tooth bounding boxes.
  • Implemented a multi-scale matching strategy to assign tooth numbers by comparing with panoramic images or tooth repositories.
  • Validated the framework on 1240 intraoral radiographs and developed a web interface for practical evaluation.

Main Results:

  • Achieved high precision and recall for tooth number assignment (0.96 via panoramic view, 0.87 via repository match).
  • Demonstrated high accuracy in arranging intraoral radiographs into an FMS template (95% for periapical, 90% for bitewing).
  • Showcased robustness with 94% accuracy in images with missing teeth and 89% with restorations.

Conclusions:

  • The AI tooth numbering model is robust, self-contained, and integrable with other diagnostic modules (e.g., caries detection).
  • Facilitates enhanced communication, documentation, and treatment planning for dentists.
  • Enables accurate, automated diagnostic information retrieval for individual teeth.